A dynamic grain flow model for a mass flow yield sensor on a combine |
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Authors: | Ryan Reinke Harry Dankowicz Jim Phelan Wonmo Kang |
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Institution: | (1) Department of Mechanical Science and Engineering, University of Illinois at Urbana-Champaign, 1206 W Green St, Urbana, IL 61801, USA;(2) John Deere Moline Technology Innovation Center, 1 John Deere Place, Moline, IL 61265, USA; |
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Abstract: | A model is developed to describe the flow of grain through a clean grain elevator system on a combine in order to facilitate
accurate mass flow rate estimation. The relationship between mass flow rate and impact force described by the model depends
upon machine operational characteristics, mechanical interactions of the grain and the machine geometry, and material properties
of the grain. The model was designed to be adaptable to varying grain conditions, such as those influenced by moisture content,
by allowing free parameters of the model to be estimated through a nonlinear regression algorithm. Simulations were performed
using discrete element modeling software and data was obtained from experiments conducted on a clean grain elevator system
at the University of Kentucky Combine Yield Monitor Test Facility to determine the ability of the model to accurately estimate
mass flow rate. The model estimated mass flow rate with a normalized root mean squared residual (NRMSR) less than 2% for discrete
element modeling simulations. For experiments involving machine components, NRMSR values were less than 3% for corn at 14%
moisture, less than 3% for corn at 21% moisture, and less than 5% for corn at 26% moisture. |
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